Abstract

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

Illustration of panoramic unwrapping of the complementary omnidirectional image. (a) Captured omnidirectional image, including the inner and the outer. (b) Panoramic image unwrapped from the inner, marked as Pano_Inner. (c) Panoramic image unwrapped from the outer, marked as Pano_Outer.

Simulation experiment based on two complementary images with luminance difference. (a), (b) Pair of source images with luminance difference. (c) Interpolated image on a row-decimated pixel set of (a) by using steering kernel regression. (d) Interpolated image on a column-decimated pixel set of (b) by using steering kernel regression. (e) Interpolated image on the combined pixel set of (c) and (d) by using steering kernel regression. (f) Interpolated image on the combined pixel set of (c) and (d) by using the proposed cross-selection kernel regression method.